Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database

• Published in: Applied multivariate research Vol. 12; no. 3; p. 163
• Main Authors: Cornell, John E., Pugh, Jacqueline A., Williams, Jr, John W., Kazis, Lewis, Lee, Austin F.S., Parchman, Michael L., Zeber, John, Pederson, Thomas, Montgomery, Kelly A., Hitchcock Noël, Polly
• Format: Journal Article
• Language: English
• Published: 13.01.2009
• ISSN: 1918-1108; 1918-1108
• DOI: 10.22329/amr.v12i3.658

Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Multimorbidity is the co-occurrence of 2 or more illnesses within a single person, which raises the question whether consistent, clinically useful multimorbidity groups exist among sets of chronic illnesses. Our purpose in this article is to describe and illustrate the application of cluster analysis to identify clinically relevant multimorbidity groups. Application of cluster analysis involves a sequence of critical methodological and analytic decisions that influence the quality and meaning of the clusters produced. We illustrate the application of cluster analysis to identify multimorbidity clusters in a set of 45 chronic illnesses in primary care patients (N = 1,327,328), with 2 or more chronic conditions, served by the Veterans Health Administration. Six clinically useful multimorbidity clusters were identified: a Metabolic Cluster, an Obesity Cluster, a Liver Cluster, a Neurovascular Cluster, a Stress Cluster and a Dual Diagnosis Cluster. Cluster analysis appears to be a useful technique for identifying multiple disease clusters and patterns of multimorbidity.